Several recent advances in smoothing and semiparametric regression are presented in this book from a unifying, Bayesian perspective. Simulation-based full Bayesian Markov chain Monte Carlo (MCMC) ...
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Several recent advances in smoothing and semiparametric regression are presented in this book from a unifying, Bayesian perspective. Simulation-based full Bayesian Markov chain Monte Carlo (MCMC) inference, as well as empirical Bayes procedures closely related to penalized likelihood estimation and mixed models, are considered here. Throughout, the focus is on semiparametric regression and smoothing based on basis expansions of unknown functions and effects in combination with smoothness priors for the basis coefficients. Beginning with a review of basic methods for smoothing and mixed models, longitudinal data, spatial data, and event history data are treated in separate chapters. Worked examples from various fields such as forestry, development economics, medicine, and marketing are used to illustrate the statistical methods covered in this book. Most of these examples have been analysed using implementations in the Bayesian software, BayesX, and some with R Codes.Less

Bayesian Smoothing and Regression for Longitudinal, Spatial and Event History Data

Ludwig FahrmeirThomas Kneib

Published in print: 2011-04-28

Several recent advances in smoothing and semiparametric regression are presented in this book from a unifying, Bayesian perspective. Simulation-based full Bayesian Markov chain Monte Carlo (MCMC) inference, as well as empirical Bayes procedures closely related to penalized likelihood estimation and mixed models, are considered here. Throughout, the focus is on semiparametric regression and smoothing based on basis expansions of unknown functions and effects in combination with smoothness priors for the basis coefficients. Beginning with a review of basic methods for smoothing and mixed models, longitudinal data, spatial data, and event history data are treated in separate chapters. Worked examples from various fields such as forestry, development economics, medicine, and marketing are used to illustrate the statistical methods covered in this book. Most of these examples have been analysed using implementations in the Bayesian software, BayesX, and some with R Codes.

At the crossroads between statistics and machine learning, probabilistic graphical models provide a powerful formal framework to model complex data. Probabilistic graphical models are probabilistic models whose graphical components denote conditional independence structures between random variables. The probabilistic framework makes it possible to deal with data uncertainty while the conditional independence assumption helps process high dimensional and complex data. Examples of probabilistic graphical models are Bayesian networks and Markov random fields, which represent two of the most popular classes of such models. With the rapid advancements of high-throughput technologies and the ever decreasing costs of these next generation technologies, a fast-growing volume of biological data of various types—the so-called omics—is in need of accurate and efficient methods for modeling, prior to further downstream analysis. Network reconstruction from gene expression data represents perhaps the most emblematic area of research where probabilistic graphical models have been successfully applied. However these models have also created renew interest in genetics, in particular: association genetics, causality discovery, prediction of outcomes, detection of copy number variations, epigenetics, etc.. For all these reasons, it is foreseeable that such models will have a prominent role to play in advances in genome-wide analyses.Less

Published in print: 2014-09-18

At the crossroads between statistics and machine learning, probabilistic graphical models provide a powerful formal framework to model complex data. Probabilistic graphical models are probabilistic models whose graphical components denote conditional independence structures between random variables. The probabilistic framework makes it possible to deal with data uncertainty while the conditional independence assumption helps process high dimensional and complex data. Examples of probabilistic graphical models are Bayesian networks and Markov random fields, which represent two of the most popular classes of such models. With the rapid advancements of high-throughput technologies and the ever decreasing costs of these next generation technologies, a fast-growing volume of biological data of various types—the so-called omics—is in need of accurate and efficient methods for modeling, prior to further downstream analysis. Network reconstruction from gene expression data represents perhaps the most emblematic area of research where probabilistic graphical models have been successfully applied. However these models have also created renew interest in genetics, in particular: association genetics, causality discovery, prediction of outcomes, detection of copy number variations, epigenetics, etc.. For all these reasons, it is foreseeable that such models will have a prominent role to play in advances in genome-wide analyses.

This book discusses novel advances in informatics and statistics in molecular cancer research. Through eight chapters it discusses specific topics in cancer research, talks about how the topics give ...
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This book discusses novel advances in informatics and statistics in molecular cancer research. Through eight chapters it discusses specific topics in cancer research, talks about how the topics give rise to development of new informatics and statistics tools, and explains how the tools can be applied. The focus of the book is to provide an understanding of key concepts and tools, rather than focusing on technical issues. A main theme is the extensive use of array technologies in modern cancer research — gene expression and exon arrays, SNP and copy number arrays and methylation arrays — to derive quantitative and qualitative statements about cancer, its progression and aetiology, and to understand how these technologies at one hand allow us learn about cancer tissue as a complex system and at the other hand allow us to pinpoint key genes and events as crucial for the development of the disease. Cancer is characterized by genetic and genomic alterations that influence all levels of the cell's machinery and function.Less

Statistics and Informatics in Molecular Cancer Research

Published in print: 2009-06-18

This book discusses novel advances in informatics and statistics in molecular cancer research. Through eight chapters it discusses specific topics in cancer research, talks about how the topics give rise to development of new informatics and statistics tools, and explains how the tools can be applied. The focus of the book is to provide an understanding of key concepts and tools, rather than focusing on technical issues. A main theme is the extensive use of array technologies in modern cancer research — gene expression and exon arrays, SNP and copy number arrays and methylation arrays — to derive quantitative and qualitative statements about cancer, its progression and aetiology, and to understand how these technologies at one hand allow us learn about cancer tissue as a complex system and at the other hand allow us to pinpoint key genes and events as crucial for the development of the disease. Cancer is characterized by genetic and genomic alterations that influence all levels of the cell's machinery and function.

An antidote to technique-oriented service courses, this book studiously avoids the recipe-book style and keeps algebraic details of specific statistical methods to the minimum extent necessary to ...
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An antidote to technique-oriented service courses, this book studiously avoids the recipe-book style and keeps algebraic details of specific statistical methods to the minimum extent necessary to understand the underlying concepts. Instead, it aims to give the reader a clear understanding of how core statistical ideas of experimental design, modelling, and data analysis are integral to the scientific method. Aimed primarily towards a range of scientific disciplines (albeit with a bias towards the biological, environmental, and health sciences), this book assumes some maturity of understanding of scientific method, but does not require any prior knowledge of statistics, or any mathematical knowledge beyond basic algebra and a willingness to come to terms with mathematical notation. Any statistical analysis of a realistically sized data-set requires the use of specially written computer software. An Appendix introduces the reader to our open-source software of choice. All of the material in the book can be understood without using either R or any other computer software.Less

Statistics and Scientific Method : An Introduction for Students and Researchers

Peter J. DiggleAmanda G. Chetwynd

Published in print: 2011-08-11

An antidote to technique-oriented service courses, this book studiously avoids the recipe-book style and keeps algebraic details of specific statistical methods to the minimum extent necessary to understand the underlying concepts. Instead, it aims to give the reader a clear understanding of how core statistical ideas of experimental design, modelling, and data analysis are integral to the scientific method. Aimed primarily towards a range of scientific disciplines (albeit with a bias towards the biological, environmental, and health sciences), this book assumes some maturity of understanding of scientific method, but does not require any prior knowledge of statistics, or any mathematical knowledge beyond basic algebra and a willingness to come to terms with mathematical notation. Any statistical analysis of a realistically sized data-set requires the use of specially written computer software. An Appendix introduces the reader to our open-source software of choice. All of the material in the book can be understood without using either R or any other computer software.